稀疏描述与结构特征融合的极化SAR斑点抑制算法

提出一种稀疏描述与结构特征相结合的极化合成孔径雷达(polarimetric synthetic aperture radar,PolSAR)图像斑点抑制算法。首先利用图像的极化信息对原图像按结构特征分类,形成分类标记图;然后采用正交匹配追踪(orthogonal-matching-pursuit,OMP)算法对图像进行稀疏分解,利用K奇异值分解 (K-singular value decomposition, K-SVD)算法对过完备字典进行训练更新,得到图像相应的训练字典和稀疏系数,重构图像；最后在重构图像中按分类图增强相应的点线目标。利用美国AIRSAR系统采集的半月湾地区数据进行实验表明:该方法在实现图像去噪的同时,能够有效的保持地物的散射特性。

PolSAR image speckle reduction based on sparse representation and structure characters

This paper presents a novel speckle reduction algorithm based on sparse representation and structure characters of the polarimetric synthetic aperture radar （PolSAR） image. Firstly, the image is classified according to the polarimetric properties to form the classification map. Secondly, the orthogonal matching pursuit （OMP） algorithm is applied to implement the sparse decomposition on PolSAR images, and the over-complete dictionary is updated by the K-singular value decomposition （K-SVD) algorithm to get the trained dictionary and sparse coefficients, with which the image without noise is reconstructed. Finally, the point and line targets are enhanced by the classification map in the reconstructed image. Experimental results with the data of the Hayward area from the AIRSAR system show that the proposed method is effective both on speckle reduction and scattering characteristics preservation.